The present disclosure relates to analysis of physiological data streams and in particular to real-time analysis of physiological data streams using textual representations.
While attending critically ill patients, each day a physician may be confronted with hundreds of variables. Clinical information systems capture physiological variables and device parameters online at least every minute. Some waveform signals such as electrocardiograms and electroencephalograms are sampled a few hundred to thousands of times per second. These physiological data samples are usually stored within the memory of the patient monitors for 72-96 hours and then discarded. Intensive Care Unit (ICU) patient records typically consist of paper notes, prepared manually, that represent 30 or 60 minutes' summaries of the enormous quantity of physiological data available. These summaries tend to be disjointed from other important data points captured in general medical records (e.g., laboratory test results, general hospital records). Physicians are required to integrate all these pieces of information manually to develop adequate representations of the state of their patients, and drive the appropriate treatment plan. Subtle yet clinically meaningful correlations are often buried within several multi-modal data streams, across long periods of time. The high dimension of this data and the time critical situations physicians are confronted with results in constant information overload. There is a lack of infrastructure support for the exploration and detection of such meaningful events in these data and as a result, medical care delivered in ICUs tends to be reactive. Physicians often react to significant events that have already occurred and affected the patient. Exploring these data points to identify the signature of such events as early as possible would allow proactive interventions before a complication negatively affects the patient.